import gradio as gr
import requests
import random
import time
import pandas as pd
from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
from game1 import read1, func1, interpre1, func1_written
from game2 import func2
from game3 import read3, func3, interpre3, func3_written
def ret_en():
return 'en'
def ret_nl():
return 'nl'
def reset_scores():
data = pd.DataFrame(
{
"Role": ["AI 🤖", "HUMAN 🙋"],
"Scores": [0, 0],
}
)
tot_scores = gr.Markdown(
'''
### <p style="text-align: center;"> Today's Scores:</p>
### <p style="text-align: center;"> 🤖 Machine   <span style="color: red;">''' + str(int(0)) + '''</span>   VS   <span style="color: green;">''' + str(int(0)) + '''</span>   Human 🙋 </p>'''
)
# scroe_human = ''' # Human: ''' + str(int(0))
# scroe_robot = ''' # Robot: ''' + str(int(0))
# tooltip=["Role", "Scores"],
return 0, 0, tot_scores
def reset_modules():
res_empty = {"original": "", "interpretation": []}
return res_empty, 0, 0, [], ""
# theme = gr.themes.Default(text_size=gr.themes.sizes.text_md).set(
# input_text_size="24px",
# )
css = """
#warning {background-color: #FFCCCB}
.feedback textarea {font-size: 24px !important}
"""
with gr.Blocks(theme=gr.themes.Default(text_size=gr.themes.sizes.text_md)) as demo:
pre_load_1 = pipeline("sentiment-analysis", model="nlptown/bert-base-multilingual-uncased-sentiment")
pre_load_2 = pipeline("text-classification", model='DTAI-KULeuven/robbert-v2-dutch-sentiment')
pre_load_3 = pipeline("text-classification", model='distilbert-base-uncased-finetuned-sst-2-english')
pre_load_4 = pipeline("text-classification", model="padmajabfrl/Gender-Classification")
num1 = gr.Number(value=0, container=False, show_label=False, visible=False)
num2 = gr.Number(value=0, container=False, show_label=False, visible=False)
num3 = gr.Number(value=0, container=False, show_label=False, visible=False)
num4 = gr.Number(value=0, container=False, show_label=False, visible=False)
with gr.Row():
with gr.Column():
placeholder = gr.Markdown(
''' ## Welcome to the Language Model Explanation Challenge! <br />
#### Language Models are powerful AI tools to understand and generate human language.<br />
#### However, they sometimes make mistakes... and it's hard to know why!<br />
#### Choose one of the tasks below ... and start to play!'''
)
with gr.Column():
# gr.Markdown(
# '''
# ### Built by [ADD GroNLP logo here]
# '''
# )
gr.Image('logo.png', height=30, width=90, min_width=80, show_label=False, show_share_button=False, interactive=False, container=False)
placeholder = gr.Markdown(
'''
Are *humans* or *machines* better at understanding language?<br />
→ Play a game against AI to find out!<br />
Does AI think like you or not at all?<br />
→ Check out the color highlighting to see which parts of the sentence are more important for the machine.<br />
Can you outsmart the AI?<br />
→ Try to write a text that will trick it into the wrong decision<br /><br />
'''
)
with gr.Tab("Like or Dislike"):
text_en = gr.Textbox(label="", value="en", visible=False)
text_nl = gr.Textbox(label="", value="nl", visible=False)
lang_selected = gr.Textbox(label="", value="", visible=False)
num_selected_1 = gr.Number(value=0, container=False, show_label=False, visible=False)
with gr.Row():
with gr.Column(scale=2):
with gr.Row():
sample_button_en = gr.Button("Click to get a review in English.", size='sm')
# gr.Markdown(''' <p style="text-align: center;"> or </p> ''')
sample_button_nl = gr.Button("Click to get a review in Dutch.", size='sm')
input_text = gr.Textbox(label="Review:", value="HELLO! Hallo!", visible=False, container=False)
interpretation1 = gr.components.Interpretation(input_text, elem_classes="feedback")
slider_1_1 = gr.Slider(label="Your rating: Dislike(0) —> Like(10)", maximum=10, step=1, container=True, min_width=200, height=80, show_label=True, interactive=True)
user_important = gr.Textbox(label="Which words are your guesses based on?", placeholder="Enter words that you think are important for the task")
with gr.Column(scale=1):
gr.Markdown(
''' ## Like or Dislike
You're given a short review of a movie, book or restaurant.
The goal of this game is to guess how *positive* the review is, from 0 (=extremely bad) to 10 (=fantastic).
* Step 1: Get an English or Dutch review and guess the corresponding score.
* Step 2: Check the score guessed by AI. Who gets the most correct answer wins.
* Step 3: Check the word highlighting to understand how AI made its decision.
'''
)
#gr.Markdown(
#''' ## Today's Scores
#'''
#)
tot_scores_1 = gr.Markdown(
'''
#### <p style="text-align: center;"> Today's Scores:</p>
#### <p style="text-align: center;"> 🤖 Machine   <span style="color: red;">''' + str(int(0)) + '''</span>   VS   <span style="color: green;">''' + str(int(0)) + '''</span>   Human 🙋 </p>'''
)
with gr.Row():
with gr.Column(scale=2):
chat_button_1 = gr.Button("Click to see AI's rating", size='sm')
slider_1_2 = gr.Slider(label="AI rating: Dislike(0) —> Like(10)", maximum=10, step=1, container=True, min_width=200, height=80, show_label=True, interactive=True)
interpre_button = gr.Button("See how AI got its rating", size='sm')
placeholder_text = gr.Textbox(label="Red higlights: Positive / Blue higlights: Negative", value="HELLO! Hallo!", visible=False)
interpretation2 = gr.components.Interpretation(placeholder_text)
with gr.Column(scale=1):
chatbot1 = gr.Chatbot(height=230, min_width=50, container=False) # height=300
####################################################################################################
gr.Markdown(''' *** ''')
gr.Markdown(
''' # Now try with your own review!
'''
)
with gr.Row():
with gr.Column(scale=2):
text_written = gr.Textbox(label="Review: ", placeholder="Enter your own review about a movie/restaurant/book.", visible=True)
# image_1_3 = gr.Image('icon_user.png', height=80, width=80, min_width=80, show_label=False, show_share_button=False, interactive=False)
slider_1_3 = gr.Slider(label="Your rating: Dislike(0) —> Like(10)", maximum=10, step=1, container=True, min_width=200, height=80, show_label=True, interactive=True)
lang_written = gr.Radio(["English", "Dutch"], label="Language:", info="In which language is the review written?")
chat_button_2 = gr.Button("Click to see AI's rating", size='sm')
placeholder_written_text = gr.Textbox(label="Red higlights: Positive / Blue higlights: Negative", value="HELLO! Hallo!", visible=False)
interpretation4 = gr.components.Interpretation(placeholder_written_text)
slider_1_4 = gr.Slider(label="AI rating: Dislike(0) —> Like(10)", maximum=10, step=1, container=True, min_width=200, height=80, show_label=True, interactive=True)
with gr.Column(scale=1):
chatbot2 = gr.Chatbot(height=350, min_width=50, container=False) # height=300
sample_button_en.click(read1, inputs=[text_en, num_selected_1], outputs=[interpretation1, lang_selected, num_selected_1])
sample_button_nl.click(read1, inputs=[text_nl, num_selected_1], outputs=[interpretation1, lang_selected, num_selected_1])
num_selected_1.change(reset_modules, outputs=[interpretation2, slider_1_1, slider_1_2, chatbot1, user_important])
chat_button_1.click(func1, inputs=[lang_selected, num_selected_1, slider_1_1, num1, num2, user_important], outputs=[slider_1_2, chatbot1, num1, num2, tot_scores_1])
interpre_button.click(interpre1, inputs=[lang_selected, num_selected_1], outputs=[interpretation2])
chat_button_2.click(func1_written, inputs=[text_written, slider_1_3, lang_written], outputs=[interpretation4, slider_1_4, chatbot2])
# with gr.Tab("Human or Machine"):
# with gr.Row():
# text_input_2 = gr.Textbox()
# text_output_2 = gr.Label()
# text_button_2 = gr.Button("Check")
with gr.Tab("Male or Female"):
num_selected_3 = gr.Number(value=0, container=False, show_label=False, visible=False)
with gr.Row():
with gr.Column(scale=2):
with gr.Row():
# gr.Markdown(''' <p style="text-align: center;"> or </p> ''')
sample_button_en_3 = gr.Button("Click to get a sentence", size='sm')
input_text_mf = gr.Textbox(label="Sentence:", value="HELLO! Hallo!", visible=False, container=False)
interpretation_mf_1 = gr.components.Interpretation(input_text_mf)
slider_3_1 = gr.Slider(label="Your guess of author gender: Male(0) ——> Female(10)", maximum=10, step=1, container=True, min_width=200, height=80, show_label=True, interactive=True)
user_important_mf = gr.Textbox(label="Which words are your guesses based on?", placeholder="Enter words that you think are important for the task")
with gr.Column(scale=1):
gr.Markdown(
''' ## Male or Female
You're given a sentence written by a person.
The goal of the game is to guess the gender of that person, from 0 (=Male) to 10 (=Female).
- Step 1: Get a sentence and guess the gender of its author.
- Step 2: Check the gender guessed by AI. Who gets the most correct answer wins.
- Step 3: Check the word highlighting to understand how AI made its decision.
'''
)
# gr.Markdown(
# ''' ## Today's Scores
# '''
# )
# tot_scores_2 = gr.Markdown(
# ''' ### <p style="text-align: center;"> 🤖 Machine   ''' + str(int(0)) + '''   VS   ''' + str(int(0)) + '''   Human 🙋 </p>'''
# )
tot_scores_2 = gr.Markdown(
'''
### <p style="text-align: center;"> Today's Scores:</p>
### <p style="text-align: center;"> 🤖 Machine   <span style="color: red;">''' + str(int(0)) + '''</span>   VS   <span style="color: green;">''' + str(int(0)) + '''</span>   Human 🙋 </p>'''
)
with gr.Row():
with gr.Column(scale=2):
chat_button_mf = gr.Button("Click to see AI's guess", size='sm')
slider_3_2 = gr.Slider(label="AI guess on author gender: Male(0) ——> Female(10)", maximum=10, step=1, container=True, min_width=200, height=80, show_label=True, interactive=True)
interpre_button_mf = gr.Button("See how AI made its guess", size='sm')
placeholder_text_mf = gr.Textbox(label="Red higlights: Female / Blue higlights: Male", value="HELLO! Hallo!", visible=False)
interpretation_mf_2 = gr.components.Interpretation(placeholder_text_mf)
with gr.Column(scale=1):
chatbot_mf_1 = gr.Chatbot(height=230, min_width=50, container=False)
####################################################################################################
gr.Markdown(''' *** ''')
gr.Markdown(
''' # Now try with your own sentence!
'''
)
with gr.Row():
with gr.Column(scale=2):
text_written_mf = gr.Textbox(label="Sentence: ", placeholder="Enter a sentence.", visible=True)
slider_3_3 = gr.Slider(label="Your guess of author gender: Male(0) ——> Female(10)", maximum=10, step=1, container=True, min_width=200, height=80, show_label=True, interactive=True)
chat_button_mf_2 = gr.Button("Click to see AI's guess", size='sm')
placeholder_written_text_mf = gr.Textbox(label="Red higlights: Female / Blue higlights: Male", value="HELLO! Hallo!", visible=False)
interpretation_mf_4 = gr.components.Interpretation(placeholder_written_text_mf)
slider_3_4 = gr.Slider(label="AI guess on author gender: Male(0) ——> Female(10)", maximum=10, container=True, min_width=200, height=80, show_label=True, interactive=True)
with gr.Column(scale=1):
chatbot_mf_2 = gr.Chatbot(height=350, min_width=50, container=False) # height=300
sample_button_en_3.click(read3, inputs=[num_selected_3], outputs=[interpretation_mf_1, num_selected_3])
num_selected_3.change(reset_modules, outputs=[interpretation_mf_2, slider_3_1, slider_3_2, chatbot_mf_1, user_important_mf])
chat_button_mf.click(func3, inputs=[num_selected_3, slider_3_1, num3, num4, user_important_mf], outputs=[slider_3_2, chatbot_mf_1, num3, num4, tot_scores_2])
interpre_button_mf.click(interpre3, inputs=[num_selected_3], outputs=[interpretation_mf_2])
chat_button_mf_2.click(func3_written, inputs=[text_written_mf, slider_3_3], outputs=[interpretation_mf_4, slider_3_4, chatbot_mf_2])
if __name__ == "__main__":
demo.launch()